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A novel methodology to classify test cases using natural language processing and imbalanced learning
Engineering Applications of Artificial Intelligence ( IF 8 ) Pub Date : 2020-08-14 , DOI: 10.1016/j.engappai.2020.103878
Sahar Tahvili , Leo Hatvani , Enislay Ramentol , Rita Pimentel , Wasif Afzal , Francisco Herrera

Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes – dependent and independent – can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases’ distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases’ specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results.



中文翻译:

一种使用自然语言处理和不平衡学习对测试用例进行分类的新颖方法

检测集成测试用例之间的依赖关系在软件测试优化领域中起着至关重要的作用。可以将测试用例分为相关和独立两大类,以用于多种测试优化目的,例如并行测试执行,测试自动化,测试用例选择和优先级划分以及测试套件缩减。由于测试用例的分布,此任务可以看作是不平衡的分类问题。依赖和独立测试用例的数量通常是不均匀的,这与测试级别,测试环境和被测系统的复杂性有关。在这项研究中,我们提出了一种新颖的方法,该方法包括两个主要步骤。首先,通过使用自然语言处理,我们分析了测试用例的规范并将其转换为数字向量。其次,通过使用获得的数据向量,我们将每个测试用例分为一个依赖类或一个独立类。我们使用不同的方法来执行有监督的学习方法来处理不平衡的数据集。在瑞典的庞巴迪运输公司的两个工业项目中,对所提出的方法的可行性和可能的​​推广进行了评估,这表明了令人鼓舞的结果。

更新日期:2020-08-14
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